{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "# Extracting Data from the StreamPipes data lake\n",
    "\n",
    "In the first tutorial ([Introduction to the StreamPipes Python client](../1-introduction-to-streampipes-python-client)) we took the first steps with the StreamPipes Python client and learned how to set everything up.\n",
    "Now we are ready to get started and want to retrieve some data out of StreamPipes.\n",
    "In this tutorial, we'll focus on the StreamPipes Data Lake, the component where StreamPipes stores data internally.\n",
    "To get started, we'll use the `client` instance created in the first tutorial."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "outputs": [],
   "source": [
    "from streampipes.client import StreamPipesClient\n",
    "from streampipes.client.config import StreamPipesClientConfig\n",
    "from streampipes.client.credential_provider import StreamPipesApiKeyCredentials"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# if you want all necessary dependencies required for this tutorial to be installed,\n",
    "# you can simply execute the following command\n",
    "%pip install matplotlib streampipes"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "outputs": [],
   "source": [
    "import os\n",
    "os.environ[\"SP_USERNAME\"] = \"admin@streampipes.apache.org\"\n",
    "os.environ[\"SP_API_KEY\"] = \"XXX\"\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [],
   "source": [
    "config = StreamPipesClientConfig(\n",
    "    credential_provider=StreamPipesApiKeyCredentials(),\n",
    "    host_address=\"localhost\",\n",
    "    https_disabled=True,\n",
    "    port=80\n",
    ")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-24 17:34:25,860 - streampipes.client.client - [INFO] - [client.py:128] [_set_up_logging] - Logging successfully initialized with logging level INFO.\n"
     ]
    }
   ],
   "source": [
    "client = StreamPipesClient(client_config=config)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "As a first step, we want to get an overview about all data available in the data lake.\n",
    "The data is stored as so-called `measures`, which refer to a data stream stored in the data lake.\n",
    "For his purpose we use the `all()` method of the `dataLakeMeasure` endpoint."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-24 17:34:25,929 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
     ]
    }
   ],
   "source": [
    "data_lake_measures = client.dataLakeMeasureApi.all()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "So let's see how many measures are available:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [
    {
     "data": {
      "text/plain": "2"
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data_lake_measures)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "All resources of the StreamPipes Python client support the standard Python expressions. If not, [please let us know](https://github.com/apache/streampipes/issues/new/choose)."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "outputs": [
    {
     "data": {
      "text/plain": "DataLakeMeasure(element_id='3cb6b5e6f107452483d1fd2ccf4bf9f9', measure_name='test', timestamp_field='s0::timestamp', event_schema=EventSchema(event_properties=[EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:EiFnkL', label='Density', description='Denotes the current density of the fluid', runtime_name='density', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=5, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:ghSkQI', label='Mass Flow', description='Denotes the current mass flow in the sensor', runtime_name='mass_flow', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=2, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:cQAUry', label='Sensor ID', description='The ID of the sensor', runtime_name='sensorId', required=False, domain_properties=['https://streampipes.org/vocabulary/examples/watertank/v1/hasSensorId'], property_scope='DIMENSION_PROPERTY', index=1, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#string', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:pbPMyL', label='Sensor Fault Flags', description='Any fault flags of the sensors', runtime_name='sensor_fault_flags', required=False, domain_properties=['http://schema.org/Boolean'], property_scope='MEASUREMENT_PROPERTY', index=6, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#boolean', measurement_unit=None, value_specification=None), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:Qmayhw', label='Temperature', description='Denotes the current temperature in degrees celsius', runtime_name='temperature', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=4, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit='http://qudt.org/vocab/unit#DegreeCelsius', value_specification=ValueSpecification(class_name='org.apache.streampipes.model.schema.QuantitativeValue', element_id=None, min_value=0, max_value=100, step=0.1)), EventProperty(class_name='org.apache.streampipes.model.schema.EventPropertyPrimitive', element_id='sp:eventproperty:YQYhjd', label='Volume Flow', description='Denotes the current volume flow', runtime_name='volume_flow', required=False, domain_properties=['http://schema.org/Number'], property_scope='MEASUREMENT_PROPERTY', index=3, runtime_id=None, runtime_type='http://www.w3.org/2001/XMLSchema#float', measurement_unit=None, value_specification=None)]), pipeline_id=None, pipeline_name=None, pipeline_is_running=False, schema_version='1.1')"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_lake_measures[-1]"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "To get a more comprehensive overview, you can take a look at the [`pandas`](https://pandas.pydata.org/) representation:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "  measure_name timestamp_field pipeline_id pipeline_name  pipeline_is_running  \\\n0    flow-rate   s0::timestamp        None          None                False   \n1         test   s0::timestamp        None          None                False   \n\n   num_event_properties  \n0                     6  \n1                     6  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>measure_name</th>\n      <th>timestamp_field</th>\n      <th>pipeline_id</th>\n      <th>pipeline_name</th>\n      <th>pipeline_is_running</th>\n      <th>num_event_properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>flow-rate</td>\n      <td>s0::timestamp</td>\n      <td>None</td>\n      <td>None</td>\n      <td>False</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>test</td>\n      <td>s0::timestamp</td>\n      <td>None</td>\n      <td>None</td>\n      <td>False</td>\n      <td>6</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(data_lake_measures.to_pandas())"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "So far, we have only retrieved metadata about the available data lake measure.\n",
    "In the following, we will access the actual data of the measure `flow-rate`.\n",
    "\n",
    "For this purpose, we will use the `get()` method of the `dataLakeMeasure` endpoint."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-24 17:34:26,020 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
     ]
    }
   ],
   "source": [
    "flow_rate_measure = client.dataLakeMeasureApi.get(identifier=\"flow-rate\")"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For further processing, the easiest way is to turn the data measure into a `pandas DataFrame`."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "flow_rate_pd = flow_rate_measure.to_pandas()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Let's see how many data points we got..."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "1000"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(flow_rate_pd)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "... and get a first overview"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "data": {
      "text/plain": "           density    mass_flow  temperature  volume_flow\ncount  1000.000000  1000.000000  1000.000000  1000.000000\nmean     45.560337     5.457014    45.480231     5.659558\nstd       3.201544     3.184959     3.132878     3.122437\nmin      40.007698     0.004867    40.000992     0.039422\n25%      42.819497     2.654101    42.754623     3.021625\n50%      45.679264     5.382355    45.435944     5.572553\n75%      48.206881     8.183144    48.248473     8.338209\nmax      50.998310    10.986015    50.964909    10.998676",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>density</th>\n      <th>mass_flow</th>\n      <th>temperature</th>\n      <th>volume_flow</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n      <td>1000.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>45.560337</td>\n      <td>5.457014</td>\n      <td>45.480231</td>\n      <td>5.659558</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.201544</td>\n      <td>3.184959</td>\n      <td>3.132878</td>\n      <td>3.122437</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>40.007698</td>\n      <td>0.004867</td>\n      <td>40.000992</td>\n      <td>0.039422</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>42.819497</td>\n      <td>2.654101</td>\n      <td>42.754623</td>\n      <td>3.021625</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>45.679264</td>\n      <td>5.382355</td>\n      <td>45.435944</td>\n      <td>5.572553</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>48.206881</td>\n      <td>8.183144</td>\n      <td>48.248473</td>\n      <td>8.338209</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>50.998310</td>\n      <td>10.986015</td>\n      <td>50.964909</td>\n      <td>10.998676</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_rate_pd.describe()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "As a final step, we want to create a plot of both attributes."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "flow_rate_pd.plot(y=[\"mass_flow\", \"temperature\"])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For data lake measurements, the `get()` method is even more powerful than simply returning all the data for a given data lake measurement. We will look at a selection of these below. The full list of supported parameters can be found in the [docs](../../reference/endpoint/api/data_lake_measure/#streampipes.endpoint.api.data_lake_measure.MeasurementGetQueryConfig). <br>\n",
    "Let's start by referring to the graph we created above, where we use only two columns of our data lake measurement. If we already know this, we can directly restrict the queried data to a subset of columns by using the `columns` parameter. <br>\n",
    "`columns` takes a list of column names as a comma-separated string:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-24 17:34:26,492 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
     ]
    },
    {
     "data": {
      "text/plain": "                         timestamp  mass_flow  temperature\n0    2023-02-24T16:19:41.472Z   3.309556    44.448483\n1    2023-02-24T16:19:41.482Z   5.608580    40.322033\n2    2023-02-24T16:19:41.493Z   7.692881    49.239639\n3    2023-02-24T16:19:41.503Z   3.632898    49.933754\n4    2023-02-24T16:19:41.513Z   0.711260    50.106617\n..                        ...        ...          ...\n995  2023-02-24T16:19:52.927Z   1.740114    46.558231\n996   2023-02-24T16:19:52.94Z   7.211723    48.048622\n997  2023-02-24T16:19:52.952Z   7.770180    48.188026\n998  2023-02-24T16:19:52.965Z   4.458602    48.280899\n999  2023-02-24T16:19:52.977Z   2.592060    47.505951\n\n[1000 rows x 3 columns]",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>timestamp</th>\n      <th>mass_flow</th>\n      <th>temperature</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>2023-02-24T16:19:41.472Z</td>\n      <td>3.309556</td>\n      <td>44.448483</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2023-02-24T16:19:41.482Z</td>\n      <td>5.608580</td>\n      <td>40.322033</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2023-02-24T16:19:41.493Z</td>\n      <td>7.692881</td>\n      <td>49.239639</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>2023-02-24T16:19:41.503Z</td>\n      <td>3.632898</td>\n      <td>49.933754</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2023-02-24T16:19:41.513Z</td>\n      <td>0.711260</td>\n      <td>50.106617</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>995</th>\n      <td>2023-02-24T16:19:52.927Z</td>\n      <td>1.740114</td>\n      <td>46.558231</td>\n    </tr>\n    <tr>\n      <th>996</th>\n      <td>2023-02-24T16:19:52.94Z</td>\n      <td>7.211723</td>\n      <td>48.048622</td>\n    </tr>\n    <tr>\n      <th>997</th>\n      <td>2023-02-24T16:19:52.952Z</td>\n      <td>7.770180</td>\n      <td>48.188026</td>\n    </tr>\n    <tr>\n      <th>998</th>\n      <td>2023-02-24T16:19:52.965Z</td>\n      <td>4.458602</td>\n      <td>48.280899</td>\n    </tr>\n    <tr>\n      <th>999</th>\n      <td>2023-02-24T16:19:52.977Z</td>\n      <td>2.592060</td>\n      <td>47.505951</td>\n    </tr>\n  </tbody>\n</table>\n<p>1000 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_rate_pd = client.dataLakeMeasureApi.get(identifier=\"flow-rate\", columns=\"mass_flow,temperature\").to_pandas()\n",
    "flow_rate_pd"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "By default, the client returns only the first one thousand records of a Data Lake measurement. This can be changed by passing a concrete value for the `limit` parameter:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-24 17:34:26,736 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
     ]
    },
    {
     "data": {
      "text/plain": "9528"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_rate_pd = client.dataLakeMeasureApi.get(identifier=\"flow-rate\", limit=10000).to_pandas()\n",
    "len(flow_rate_pd)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "If you want your data to be selected by time of occurrence rather than quantity, you can specify your time window by passing the `start_date` and `end_date` parameters:"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-02-24 17:34:26,899 - streampipes.endpoint.endpoint - [INFO] - [endpoint.py:167] [_make_request] - Successfully retrieved all resources.\n"
     ]
    },
    {
     "data": {
      "text/plain": "<Figure size 640x480 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "flow_rate_pd = client.dataLakeMeasureApi.get(\n",
    "    identifier=\"flow-rate\",\n",
    "    start_date=datetime(year=2023, month=2, day=24, hour=17, minute=21, second=0),\n",
    "    end_date=datetime(year=2023, month=2, day=24, hour=17, minute=21, second=1),\n",
    "    ).to_pandas()\n",
    "flow_rate_pd.plot(y=[\"mass_flow\", \"temperature\"])\n",
    "plt.show()"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "... from this point on, we leave all future processing of the data up to your creativity.\n",
    "Keep in mind: the general syntax used in this tutorial (`all()`, `to_pandas()`, `get()`) applies to all endpoints and associated resources of the StreamPipes Python client.\n",
    "\n",
    "If you get further and create exciting stuff with data extracted from StreamPipes please [let us know](https://github.com/apache/streampipes/discussions/categories/show-and-tell).\n",
    "We are thrilled to see what you as a community will build with the provided client.\n",
    "Furthermore, don't hesitate to discuss [feature requests](https://github.com/apache/streampipes/discussions/812) to extend the current functionality with us."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "For now, that's all about the StreamPipes client. Read the next tutorial ([Getting live data from the StreamPipes data stream](../3-getting-live-data-from-the-streampipes-data-stream)) if you are interested in making use of the powerful [StreamPipes functions](https://streampipes.apache.org/docs/extend-sdk-functions.html) to interact with StreamPipes in an event-based manner."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "How do you like this tutorial?\n",
    "We hope you like it and would love to receive some feedback from you.\n",
    "Just go to our [GitHub discussion page](https://github.com/apache/streampipes/discussions) and let us know your impression.\n",
    "We'll read and react to them all, we promise!"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [],
   "metadata": {
    "collapsed": false
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  },
  "license": "https://www.apache.org/licenses/LICENSE-2.0"
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
